Content aware bit flipping decoder

    公开(公告)号:US11057059B1

    公开(公告)日:2021-07-06

    申请号:US16744061

    申请日:2020-01-15

    IPC分类号: H03M13/45 H03M13/11 G06F11/10

    摘要: Examples described herein relate generally to content aware bit flipping decoders. An example device includes a decoder. The decoder is configured to: process one or more flip thresholds based on statistics of data to be decoded; and perform a bit flipping algorithm on the data using the one or more processed flip thresholds. Other examples relate to methods of processing one or more flip thresholds based on statistics of data to be decoded and performing a bit flipping algorithm on the data using the one or more processed flip thresholds.

    Data driven ICAD graph generation

    公开(公告)号:US10862512B2

    公开(公告)日:2020-12-08

    申请号:US16452466

    申请日:2019-06-25

    IPC分类号: H03M13/35 G06F11/10 G11C29/52

    摘要: A storage device may include a decoder configured to connect bits to a content node based on content-aware decoding process. The content-aware decoding process may be dynamic and determine connection structures of bits and content nodes based on patterns in data. In some cases, the decoder may connect non-adjacent bits to a content node based on a content-aware decoding process. In other cases, the decoder may connect a first number of bits to a first content node and a second number of bits to a second content node. In such cases, the first number of bits and the second number of bits are a different number.

    MEMORY DEVICE WITH ENHANCED ERROR CORRECTION

    公开(公告)号:US20200235757A1

    公开(公告)日:2020-07-23

    申请号:US16254575

    申请日:2019-01-22

    IPC分类号: H03M13/37 G06F11/10

    摘要: Disclosed herein are memory devices, systems, and methods of content-aware decoding of encoded data. In one aspect, an encoded data chunk is received and one or more characteristics, such as source statistics, are determined. A similar data chunk (that may, e.g., contain data of a similar type) with comparable statistics may be sought. The similar data chunk may, for example, have source statistics that are positively correlated to the source statistics of the encoded data chunk to be decoded. Decoder parameters for the encoded data may be set to correspond with decoder parameters suited to the similar data chunk. The encoded data chunk is decoded using the new decoder parameters. Decoding encoded data based on content can enhance performance, reducing decoding latency and/or power consumption.

    Content aware decoding using shared data statistics

    公开(公告)号:US11528038B2

    公开(公告)日:2022-12-13

    申请号:US17211605

    申请日:2021-03-24

    IPC分类号: H03M13/35 G06F11/10

    摘要: A method and apparatus for content aware decoding utilizes a pool of decoders shared data statistics. Each decoder generates statistical data of content it decodes and provides these statistics to a joint statistics pool. As codewords arrive at the decoder pool, the joint statistics are utilized to estimate or predict any corrupted or missing bit values. Codewords may be assigned to a specific decoder, such as a tier 1 decoder, a tier 2 decoder, or a tier 3 decoder, based on a syndrome weight or a bit error rate. The assigned decoder updates the joint statistics pool after processing the codeword. In some embodiments, each decoder may additionally maintain local statistics regarding codewords, and use the local statistics when there is a statistically significant mismatch between the local statistics and the joint statistics pool.

    Parameterized iterative message passing decoder

    公开(公告)号:US10382067B2

    公开(公告)日:2019-08-13

    申请号:US15617629

    申请日:2017-06-08

    摘要: Technology is described herein for learning parameters for a parameterized iterative message passing decoder, and to a corresponding parameterized iterative message passing decoder. Learning the parameters may adapt the decoder to statistical dependencies introduced by the specific code's graph. Taking into account the statistical dependencies may allow the code to be shorter and/or denser. Note that the statistical dependencies in the graph may be extremely complex. Machine learning may be used to learn the parameters. The parameters may be learned when decoding data stored in the memory device. Learning the parameters may adapt the decoder to properties of data stored in the memory device, physical properties of the memory device, and/or patterns in host data.